Literature DB >> 34722861

Primary care clinicians' perspectives on clinical decision support to enhance outcomes of online obesity treatment in primary care: A qualitative formative evaluation.

Hallie M Espel-Huynh1,2, Carly M Goldstein1,2, Olivia L Finnegan1,3, A Rani Elwy2,4, Rena R Wing1,2, J Graham Thomas1,2.   

Abstract

OBJECTIVE: Online behavioral treatment for obesity produces clinically-meaningful weight losses among many primary care patients. However, some patients experience poor outcomes (i.e., failure to enroll post-referral, poor weight loss, or premature disengagement). This study sought to understand primary care clinicians' perceived utility of a clinical decision support system (CDSS) that would alert clinicians to patients' risk for poor outcome and guide clinician-delivered rescue interventions to reduce risk.
METHODS: Qualitative formative evaluation was conducted in the context of an ongoing pragmatic clinical trial implementing online obesity treatment in primary care. Interviews were conducted with 14 nurse care managers (NCMs) overseeing patients' online obesity treatment. Interviews inquired about the potential utility of CDSS in primary care, desired alert frequency/format, and priorities for alert types (non-enrollment, poor weight loss, and/or early disengagement). We used matrix analysis to generate common themes across interviews.
RESULTS: Nearly all NCMs viewed CDSS as potentially helpful in clinical practice. Alerts for patients at risk for disengagement were of highest priority, though all alert types were generally viewed as desirable. Regarding frequency and delivery mode of patient alerts, NCMs wanted to balance the need for prompt patient intervention with minimizing clinician burden. Concerns about CDSS emerged, including insufficient time to respond promptly and adequately to alerts and the need to involve other support staff for patients requiring ongoing rescue intervention.
CONCLUSIONS: NCMs view CDSS for online obesity treatment as potentially feasible and clinically useful. For optimal implementation in primary care, CDSS must minimize clinician burden and facilitate collaborative care.

Entities:  

Keywords:  decision support systems—clinical; obesity; primary health care; weight loss

Year:  2021        PMID: 34722861      PMCID: PMC8555757          DOI: 10.1007/s41347-021-00206-6

Source DB:  PubMed          Journal:  J Technol Behav Sci        ISSN: 2366-5963


  37 in total

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Authors:  Cheryl B Stetler; Marcia W Legro; Carolyn M Wallace; Candice Bowman; Marylou Guihan; Hildi Hagedorn; Barbara Kimmel; Nancy D Sharp; Jeffrey L Smith
Journal:  J Gen Intern Med       Date:  2006-02       Impact factor: 5.128

2.  Effectiveness of a Cloud-Based EHR Clinical Decision Support Program for Body Mass Index (BMI) Screening and Follow-up.

Authors:  Shruti Gangadhar; Nam Nguyen; James W Pesuit; Alina N Bogdanov; Lee Kallenbach; Jessica Ken; Joe Vasey; Richard M Loomis
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

3.  Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review.

Authors:  D L Hunt; R B Haynes; S E Hanna; K Smith
Journal:  JAMA       Date:  1998-10-21       Impact factor: 56.272

4.  An internet-based intervention with brief nurse support to manage obesity in primary care (POWeR+): a pragmatic, parallel-group, randomised controlled trial.

Authors:  Paul Little; Beth Stuart; Fd Richard Hobbs; Jo Kelly; Emily R Smith; Katherine J Bradbury; Stephanie Hughes; Peter W F Smith; Michael V Moore; Mike E J Lean; Barrie M Margetts; Chris D Byrne; Simon Griffin; Mina Davoudianfar; Julie Hooper; Guiqing Yao; Shihua Zhu; James Raftery; Lucy Yardley
Journal:  Lancet Diabetes Endocrinol       Date:  2016-07-26       Impact factor: 32.069

Review 5.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.

Authors:  Kensaku Kawamoto; Caitlin A Houlihan; E Andrew Balas; David F Lobach
Journal:  BMJ       Date:  2005-03-14

6.  The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies.

Authors:  Asnawi Abdullah; Anna Peeters; Maximilian de Courten; Johannes Stoelwinder
Journal:  Diabetes Res Clin Pract       Date:  2010-05-20       Impact factor: 5.602

Review 7.  Initial Weight Loss Response as an Indicator for Providing Early Rescue Efforts to Improve Long-term Treatment Outcomes.

Authors:  Jessica L Unick; Christine A Pellegrini; Kathryn E Demos; Leah Dorfman
Journal:  Curr Diab Rep       Date:  2017-09       Impact factor: 4.810

8.  Effectiveness of an App and Provider Counseling for Obesity Treatment in Primary Care.

Authors:  Gary G Bennett; Dori Steinberg; Sandy Askew; Erica Levine; Perry Foley; Bryan C Batch; Laura P Svetkey; Hayden B Bosworth; Elaine M Puleo; Ashley Brewer; Abigail DeVries; Heather Miranda
Journal:  Am J Prev Med       Date:  2018-10-22       Impact factor: 5.043

9.  An automated internet behavioral weight-loss program by physician referral: a randomized controlled trial.

Authors:  J Graham Thomas; Tricia M Leahey; Rena R Wing
Journal:  Diabetes Care       Date:  2014-11-17       Impact factor: 19.112

10.  Can rapid approaches to qualitative analysis deliver timely, valid findings to clinical leaders? A mixed methods study comparing rapid and thematic analysis.

Authors:  Beck Taylor; Catherine Henshall; Sara Kenyon; Ian Litchfield; Sheila Greenfield
Journal:  BMJ Open       Date:  2018-10-08       Impact factor: 2.692

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  1 in total

1.  Pragmatic implementation of a fully automated online obesity treatment in primary care.

Authors:  J Graham Thomas; Emily Panza; Hallie M Espel-Huynh; Carly M Goldstein; Kevin O'Leary; Noah Benedict; Albert J Puerini; Rena R Wing
Journal:  Obesity (Silver Spring)       Date:  2022-08       Impact factor: 9.298

  1 in total

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